IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v293y2024ics0360544224003918.html
   My bibliography  Save this article

Reduction of electricity consumption in an AHU using mathematical modelling for controller tuning

Author

Listed:
  • García Vázquez, C.A.
  • Cotfas, D.T.
  • González Santos, A.I.
  • Cotfas, P.A.
  • León Ávila, B.Y.

Abstract

Energy consumed by HVAC (heating, ventilating and air conditioning) systems represents a considerable part of the energy consumed in buildings. This paper focuses on achieving energy efficiency, through automatic control strategies, of HVAC systems in the biopharmaceutical industry, a sector little covered by previous studies, mainly focused on residential and commercial buildings. The system under study is an air handling unit (AHU). The main contributions of this research are the obtaining of a dynamic, multivariable, and non-linear model of the AHU, proposing a relatively simple structure and the procedure to estimate its parameters; a non-linear static model of the power consumption of its bank of electrical resistors, also simple, but useful to guide the PID tuning toward energy efficiency; and the approximation to the model of a PID controller whose control low is unknown. The methods proposed to obtain the models and to perform the simulations are also provided. Results for a close-to-reality simulation scenario that suggests the possibility of reducing the power consumed by the resistor bank by 29 % are presented. The use of an industrial PI control algorithm, instead of the classical textbook algorithm, also distinguishes this work from others.

Suggested Citation

  • García Vázquez, C.A. & Cotfas, D.T. & González Santos, A.I. & Cotfas, P.A. & León Ávila, B.Y., 2024. "Reduction of electricity consumption in an AHU using mathematical modelling for controller tuning," Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s0360544224003918
    DOI: 10.1016/j.energy.2024.130619
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224003918
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130619?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xiaowu Li & Kun Sun & Hongbo Fan & Zihan He, 2023. "Real-Time Cattle Pose Estimation Based on Improved RTMPose," Agriculture, MDPI, vol. 13(10), pages 1-18, October.
    2. Li, Xiaoting & Joe, Harry, 2023. "Estimation of multivariate tail quantities," Computational Statistics & Data Analysis, Elsevier, vol. 185(C).
    3. Shaomin Li & Kangning Wang & Yong Xu, 2023. "Robust estimation for nonrandomly distributed data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 493-509, June.
    4. Jonathan Roth & Pedro H. C. Sant’Anna, 2023. "Efficient Estimation for Staggered Rollout Designs," Journal of Political Economy Microeconomics, University of Chicago Press, vol. 1(4), pages 669-709.
    5. Wenjuan Li & Wenying Wang & Jingsi Chen & Weidong Rao, 2023. "Aggregate Kernel Inverse Regression Estimation," Mathematics, MDPI, vol. 11(12), pages 1-10, June.
    6. Song, Kwonsik & Jang, Youjin & Park, Moonseo & Lee, Hyun-Soo & Ahn, Joseph, 2020. "Energy efficiency of end-user groups for personalized HVAC control in multi-zone buildings," Energy, Elsevier, vol. 206(C).
    7. Wen, Shuqing & Zhang, Weirong & Sun, Yifu & Li, Zhenxi & Huang, Boju & Bian, Shouguo & Zhao, Lin & Wang, Yan, 2023. "An enhanced principal component analysis method with Savitzky–Golay filter and clustering algorithm for sensor fault detection and diagnosis," Applied Energy, Elsevier, vol. 337(C).
    8. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Peng, Pei & Li, Wenqiang & Shi, Xing, 2023. "Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level," Energy, Elsevier, vol. 263(PB).
    9. Yukun Ma & Pedro H. C. Sant'Anna & Yuya Sasaki & Takuya Ura, 2023. "Doubly Robust Estimators with Weak Overlap," Papers 2304.08974, arXiv.org, revised Apr 2023.
    10. Mawson, Victoria Jayne & Hughes, Ben Richard, 2020. "Thermal modelling of manufacturing processes and HVAC systems," Energy, Elsevier, vol. 204(C).
    11. Salins, Sampath Suranjan & Kumar, Sreejith Sanal & Thommana, Antony John Jose & Vincent, Vivian Christo & Tejero-González, Ana & Kumar, Shiva, 2023. "Performance characterization of an adaptive-controlled air handling unit to achieve thermal comfort in Dubai climate," Energy, Elsevier, vol. 273(C).
    12. Homod, Raad Z. & Gaeid, Khalaf S. & Dawood, Suroor M. & Hatami, Alireza & Sahari, Khairul S., 2020. "Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings," Applied Energy, Elsevier, vol. 271(C).
    13. Guanjing Lin & Armando Casillas & Maggie Sheng & Jessica Granderson, 2023. "Performance Evaluation of an Occupancy-Based HVAC Control System in an Office Building," Energies, MDPI, vol. 16(20), pages 1-21, October.
    14. Adrian Chojecki & Arkadiusz Ambroziak & Piotr Borkowski, 2023. "Fuzzy Controllers Instead of Classical PIDs in HVAC Equipment: Dusting Off a Well-Known Technology and Today’s Implementation for Better Energy Efficiency and User Comfort," Energies, MDPI, vol. 16(7), pages 1-21, March.
    15. Tillmann, Andreas M. & Joormann, Imke & Ammann, Sabrina C.L., 2023. "Reproducible air passenger demand estimation," Journal of Air Transport Management, Elsevier, vol. 112(C).
    16. Khan, Muhammad Waqas & Choudhry, Mohammad Ahmad & Zeeshan, Muhammad & Ali, Ahsan, 2015. "Adaptive fuzzy multivariable controller design based on genetic algorithm for an air handling unit," Energy, Elsevier, vol. 81(C), pages 477-488.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(C).
    2. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    3. Esmaeilzadeh, Ahmad & Deal, Brian & Yousefi-Koma, Aghil & Zakerzadeh, Mohammad Reza, 2023. "How combination of control methods and renewable energies leads a large commercial building to a zero-emission zone – A case study in U.S," Energy, Elsevier, vol. 263(PD).
    4. Leehter Yao & Jin-Hao Huang, 2019. "Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center," Energies, MDPI, vol. 12(8), pages 1-16, April.
    5. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
    6. Brugués, Felipe & Brugués, Javier & Giambra, Samuele, 2024. "Political connections and misallocation of procurement contracts: Evidence from Ecuador," Journal of Development Economics, Elsevier, vol. 170(C).
    7. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    8. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    9. Cl'ement de Chaisemartin & Xavier D'Haultf{oe}uille, 2021. "Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey," Papers 2112.04565, arXiv.org, revised Jun 2022.
    10. Chen, Jianguo & Han, Xuebing & Sun, Tao & Zheng, Yuejiu, 2024. "Analysis and prediction of battery aging modes based on transfer learning," Applied Energy, Elsevier, vol. 356(C).
    11. Liu, Yiren & Zhao, Xiangyu & Qin, S. Joe, 2024. "Dynamically engineered multi-modal feature learning for predictions of office building cooling loads," Applied Energy, Elsevier, vol. 355(C).
    12. Benjamin W. Arold & M. Danish Shakeel, 2021. "The Unintended Effects of the Common Core State Standards on Non-Targeted Subjects," ifo Working Paper Series 354, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    13. Lee, Minjung & Ham, Jeonggyun & Lee, Jeong-Won & Cho, Honghyun, 2023. "Analysis of thermal comfort, energy consumption, and CO2 reduction of indoor space according to the type of local heating under winter rest conditions," Energy, Elsevier, vol. 268(C).
    14. Carattini, Stefano & Figge, Béla & Gordan, Alexander & Löschel, Andreas, 2024. "Municipal building codes and the adoption of solar photovoltaics," Journal of Environmental Economics and Management, Elsevier, vol. 124(C).
    15. Homod, Raad Z. & Togun, Hussein & Ateeq, Adnan A. & Al-Mousawi, Fadhel Noraldeen & Yaseen, Zaher Mundher & Al-Kouz, Wael & Hussein, Ahmed Kadhim & Alawi, Omer A. & Goodarzi, Marjan & Ahmadi, Goodarz, 2022. "An innovative clustering technique to generate hybrid modeling of cooling coils for energy analysis: A case study for control performance in HVAC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    16. Jia, Zhiyang & Jin, Xinqiao & Lyu, Yuan & Xue, Qi & Du, Zhimin, 2024. "A novel load allocation strategy based on the adaptive chiller model with data augmentation," Energy, Elsevier, vol. 309(C).
    17. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
    18. Sun, Chunhua & Zhang, Haixiang & Cao, Shanshan & Xia, Guoqiang & Zhong, Jian & Wu, Xiangdong, 2023. "A hierarchical classifying and two-step training strategy for detection and diagnosis of anormal temperature in district heating system," Applied Energy, Elsevier, vol. 349(C).
    19. He, Xianya & Huang, Jingzhi & Liu, Zekun & Lin, Jian & Jing, Rui & Zhao, Yingru, 2023. "Topology optimization of thermally activated building system in high-rise building," Energy, Elsevier, vol. 284(C).
    20. Sulaiman, Mohd Herwan & Mustaffa, Zuriani, 2024. "Chiller energy prediction in commercial building: A metaheuristic-Enhanced deep learning approach," Energy, Elsevier, vol. 297(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:293:y:2024:i:c:s0360544224003918. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.